Statistical Learning with Sparsity: The Lasso and Generalizations (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)
| Author | : | |
| Rating | : | 4.33 (965 Votes) |
| Asin | : | 1498712169 |
| Format Type | : | paperback |
| Number of Pages | : | 367 Pages |
| Publish Date | : | 2017-05-29 |
| Language | : | English |
DESCRIPTION:
Excellent! Very helpful!! Exactly what I need, how I wish I could have found it earlier.. "Bet on sparsity approach in Machine Learning." according to Vladislavs Dovgalecs. This book is about bet on sparsity. In Machine Learning, there are plenty of approaches that might work on data of interest. The accent is on cases p>>n and one wants to get more interpretable models.. "Five Stars" according to sv_81. Great product, fast delivery, thanks!
They focus on the Lasso technique as an alternative to the standard least-squares method."Zentralblatt MATH 1319"The book includes all the major branches of statistical learning. "The authors study and analyze methods using the sparsity property of some statistical models in order to recover the underlying signal in a dataset. For each topic, the authors first give a concise introduction of the basic problem, evaluate conventional methods, pointing out their deficiencies, and then introduce a method based on sp
He has authored five books, co-authored three books, and published over 200 research articles. Professor Hastie is known for his research in applied statistics, particularly in the fields of data mining, bioinformatics, and machine learning. Professor Tibshirani was a recipient of the prestigious COPSS Presidents’ Award in 1996 and was elec
In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data. They discuss the application of 1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. It concl